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18 February 2020 Synthetic aperture radar autofocus based on phaseless measurements
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We introduce a new framework for compressed sensing (CS) in synthetic aperture radar (SAR) imaging in the case of model error. Conventional CS-based autofocus methods solve a joint optimization problem to achieve both model error parameter estimation and SAR image formation simultaneously. Owing to the possibly nonconvex feature of the joint optimization problem, however, these algorithms may get stuck in local optima having large phase errors and thus fail to reconstruct the image. In contrast, we use phaseless measurements and pose imaging as a convex optimization problem. To solve the convex problem, we use the alternating direction method of multipliers-based approach, which is computationally efficient and easy to implement. The results from simulations with both point targets and extended targets validate the effectiveness of the proposed method.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2020/$28.00 © 2020 SPIE
Yanwei Yin, Falin Liu, Zheng Wang, Hao Han, and Yuanhang Jia "Synthetic aperture radar autofocus based on phaseless measurements," Journal of Applied Remote Sensing 14(1), 014515 (18 February 2020).
Received: 16 September 2019; Accepted: 28 January 2020; Published: 18 February 2020

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